public static void Run() { int batch_size = 128; int num_classes = 10; int epochs = 100; // the data, split between train and test sets var((x_train, y_train), (x_test, y_test)) = Cifar10.LoadData(); Console.WriteLine("x_train shape: " + x_train.shape); Console.WriteLine(x_train.shape[0] + " train samples"); Console.WriteLine(x_test.shape[0] + " test samples"); // convert class vectors to binary class matrices y_train = Util.ToCategorical(y_train, num_classes); y_test = Util.ToCategorical(y_test, num_classes); // Build CNN model var model = new Sequential(); model.Add(new Conv2D(32, kernel_size: (3, 3).ToTuple(), padding: "same", input_shape: new Shape(32, 32, 3))); model.Add(new Activation("relu")); model.Add(new Conv2D(32, (3, 3).ToTuple())); model.Add(new Activation("relu")); model.Add(new MaxPooling2D(pool_size: (2, 2).ToTuple())); model.Add(new Dropout(0.25)); model.Add(new Conv2D(64, kernel_size: (3, 3).ToTuple(), padding: "same")); model.Add(new Activation("relu")); model.Add(new Conv2D(64, (3, 3).ToTuple())); model.Add(new Activation("relu")); model.Add(new MaxPooling2D(pool_size: (2, 2).ToTuple())); model.Add(new Dropout(0.25)); model.Add(new Flatten()); model.Add(new Dense(512)); model.Add(new Activation("relu")); model.Add(new Dropout(0.5)); model.Add(new Dense(num_classes)); model.Add(new Activation("softmax")); model.Compile(loss: "categorical_crossentropy", optimizer: new RMSprop(lr: 0.0001f, decay: 1e-6f), metrics: new string[] { "accuracy" }); x_train = x_train.astype(np.float32); x_test = x_test.astype(np.float32); x_train /= 255; x_test /= 255; model.Fit(x_train, y_train, batch_size: batch_size, epochs: epochs, verbose: 1, validation_data: new NDarray[] { x_test, y_test }, shuffle: true); //Save model and weights //string model_path = "./model.json"; //string weight_path = "./weights.h5"; //string json = model.ToJson(); //File.WriteAllText(model_path, json); //model.SaveWeight(weight_path); model.Save("model.h5"); model.SaveTensorflowJSFormat("./"); //Score trained model. var score = model.Evaluate(x_test, y_test, verbose: 0); Console.WriteLine("Test loss:" + score[0]); Console.WriteLine("Test accuracy:" + score[1]); }